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\section{Conclusion}
\label{sec:conclusion}
In this assignment we investigated two classifiers for the movie review dataset, 
namely multinomial model for Naive Bayes and linear SVM. We build the classifiers 
so that we are able to correctly identify the positive reviews from the negative ones. 
In order to achieve this classification of polarity, we performed several experiments 
with feature selection, feature transformation and feature generation and 
observed their effects on accuracy of the models. We found that linear SVM performs
better than the multinomial model of Naive Bayes in most of the experiments.
We also found that among the different feature selection techniques stemming reduces accuracy,
and that using a binary term frequency instead of TF-IDF significanly improves accuracy of
both the classifiers. We found that our results for SVM are similar to that reported in other published
findings using the same dataset.


